subject:"\[R\-sig\-phylo\] Threshold models using threshBayes vs MCMCglmmRAM"

Re: [R-sig-phylo] Threshold models using threshBayes vs MCMCglmmRAM

2016-12-15 Thread Jarrod Hadfield


Hi Chris,

OK - stick with the RAM model, the h2 is so high you will run into 
numerical issues otherwise. In the two-trait model you might want to add 
in us(at.level(trait,1)):units into the random effects (make sure it is 
not the last term in the random formula) in case log.dep has a h2 
substantially less than 1. Having a multi-level response will help with 
power so I would go for it. threshBayes does handle ordinal responses 
but you would probably have to run it for a VERY long time to sample the 
posterior adequately.


Cheers,

Jarrod

On 16/12/2016 07:11, Chris Mull wrote:

Hi Jarrod,
I hadn't appreciated that the clustering of reproductive modes on the 
tree might limit out ability to detect some of these relationships. 
This is in fact a step in testing reproduction as an ordinal variable 
(egg-laying, lecithotrophic live-bearing, and matrotrophic 
live-bearing) which follows gradients of depth and latitude, and 
threshBayes cannot handle ordinal variables. Perhaps treating the data 
this way will help given more transitions. I have run the model in 
MCMCglmm and have appended the summary and attached the histogram of 
the liabilities. Thanks so much for your help with this...


summary(dep2)
Iterations = 3001:12991
 Thinning interval  = 10
 Sample size  = 1000

 DIC: 31.2585

 G-structure:  ~animal

   post.mean l-95% CI u-95% CI eff.samp
animal 82.1835.88140.16.266

 R-structure:  ~units

  post.mean l-95% CI u-95% CI eff.samp
units 1110

 Location effects: parity ~ log.med.depth

  post.mean l-95% CI u-95% CI eff.samp pMCMC
(Intercept)  0.4250 -13.5697  13.791328.54 0.946
log.med.depth   -0.3601  -4.4399   3.802216.48 0.862

On Thu, Dec 15, 2016 at 11:10 PM, Jarrod Hadfield > wrote:


Hi Chris,

I think MCMCglmm is probably giving you the right answer. There
are huge chunks of the phylogeny that are either egg-laying and
live-bearing. The non-phylogenetic model shows a strong
relationship between reproductive mode and depth, and that might
be causal or it might just be because certain taxa tend to live at
greater depths and *happen to have* the same reproductive mode.
There's not much information in the phylogenetic spread of
reproductive modes to distinguish between these hypotheses, hence
the wide confidence intervals.   Just to be sure can you

a) just perform independent contrasts (not really suitable for
binary data, but probably won't give you an answer far off the
truth and is a nice simple sanity check).

b) using MCMCglmm (not MCMCglmmRAM) fit

prior.dep2<-=list(R=list(V=diag(1), fix=1),
G=list(G1=list(V=diag(1), nu=0.002)))

dep2<-MCMCglmm(parity~log.med.depth,
   random=~animal,
   rcov=~units,
   pedigree=shark.tree,
   data=traits,
   prior=prior.dep2,
   pr=TRUE,
   pl=TRUE,
   family="threshold")

an send me the summary and hist(dep2$Liab)

Cheers,

Jarrod



On 16/12/2016 07:02, Jarrod Hadfield wrote:


Hi Chris,

I think ngen in threshbayes is not the number of full iterations
(i.e. a full update of all parameters), but the number of full
iterations multiplied by the number of nodes (2n-1). With n=600
species this means threshbayes has only really done about 8,000
iterations (i.e. about 1000X less than MCMCglmm). Simulations
suggest threshbayes is about half as efficient per full iteration
as MCMCglmm which means that it may have only collected
0.5*1132/1200 = 0.47 effective samples from the posterior. The
very extreme value and the surprisingly tight credible intervals
(+/-0.007) also suggest a problem.

**However**, the low effective sample size for the covariance in
the MCMglmm model is also worrying given the length of chain, and
may point to potential problems. Are egg-laying/live-bearing
scattered throughout the tree, or do they tend to aggregate a
lot? Can you send me the output from:

prior.dep<-=list(R=list(V=diag(1)*1e-15, fix=1),
G=list(G1=list(V=diag(1), fix=1)))

dep<-MCMCglmm(parity~log.med.depth,
   random=~animal,
   rcov=~units,
   pedigree=shark.tree,
   reduced=TRUE,
   data=traits,
   prior=prior2,
   pr=TRUE,
   pl=TRUE,
   family="threshold")

summary(dep)

summary(glm(parity~log.med.depth, data=traits,
family=binomial(link=probit)))

Cheers,

Jarrod



On 15/12/2016 20:59, Chris Mull wrote:

Hi All,
I am trying to look at the

Re: [R-sig-phylo] Threshold models using threshBayes vs MCMCglmmRAM

2016-12-15 Thread Chris Mull

Hi Jarrod,
I hadn't appreciated that the clustering of reproductive modes on the tree
might limit out ability to detect some of these relationships. This is in
fact a step in testing reproduction as an ordinal variable (egg-laying,
lecithotrophic live-bearing, and matrotrophic live-bearing) which follows
gradients of depth and latitude, and threshBayes cannot handle ordinal
variables. Perhaps treating the data this way will help given more
transitions. I have run the model in MCMCglmm and have appended the summary
and attached the histogram of the liabilities. Thanks so much for your help
with this...

summary(dep2)
Iterations = 3001:12991
 Thinning interval  = 10
 Sample size  = 1000

 DIC: 31.2585

 G-structure:  ~animal

   post.mean l-95% CI u-95% CI eff.samp
animal 82.1835.88140.16.266

 R-structure:  ~units

  post.mean l-95% CI u-95% CI eff.samp
units 1110

 Location effects: parity ~ log.med.depth

  post.mean l-95% CI u-95% CI eff.samp pMCMC
(Intercept)  0.4250 -13.5697  13.791328.54 0.946
log.med.depth   -0.3601  -4.4399   3.802216.48 0.862

On Thu, Dec 15, 2016 at 11:10 PM, Jarrod Hadfield 
wrote:

> Hi Chris,
>
> I think MCMCglmm is probably giving you the right answer. There are huge
> chunks of the phylogeny that are either egg-laying and live-bearing. The
> non-phylogenetic model shows a strong relationship between reproductive
> mode and depth, and that might be causal or it might just be because
> certain taxa tend to live at greater depths and *happen to have* the same
> reproductive mode. There's not much information in the phylogenetic spread
> of reproductive modes to distinguish between these hypotheses, hence the
> wide confidence intervals.   Just to be sure can you
>
> a) just perform independent contrasts (not really suitable for binary
> data, but probably won't give you an answer far off the truth and is a nice
> simple sanity check).
>
> b) using MCMCglmm (not MCMCglmmRAM) fit
>
> prior.dep2<-=list(R=list(V=diag(1), fix=1), G=list(G1=list(V=diag(1),
> nu=0.002)))
>
> dep2<-MCMCglmm(parity~log.med.depth,
>random=~animal,
>rcov=~units,
>pedigree=shark.tree,
>data=traits,
>prior=prior.dep2,
>pr=TRUE,
>pl=TRUE,
>family="threshold")
>
> an send me the summary and hist(dep2$Liab)
>
> Cheers,
>
> Jarrod
>
>
>
> On 16/12/2016 07:02, Jarrod Hadfield wrote:
>
> Hi Chris,
>
> I think ngen in threshbayes is not the number of full iterations (i.e. a
> full update of all parameters), but the number of full iterations
> multiplied by the number of nodes (2n-1). With n=600 species this means
> threshbayes has only really done about 8,000 iterations (i.e. about 1000X
> less than MCMCglmm). Simulations suggest threshbayes is about half as
> efficient per full iteration as MCMCglmm which means that it may have only
> collected 0.5*1132/1200 = 0.47 effective samples from the posterior. The
> very extreme value and the surprisingly tight credible intervals (+/-0.007)
> also suggest a problem.
>
> **However**, the low effective sample size for the covariance in the
> MCMglmm model is also worrying given the length of chain, and may point to
> potential problems. Are egg-laying/live-bearing scattered throughout the
> tree, or do they tend to aggregate a lot? Can you send me the output from:
>
> prior.dep<-=list(R=list(V=diag(1)*1e-15, fix=1),
> G=list(G1=list(V=diag(1), fix=1)))
>
> dep<-MCMCglmm(parity~log.med.depth,
>random=~animal,
>rcov=~units,
>pedigree=shark.tree,
>reduced=TRUE,
>data=traits,
>prior=prior2,
>pr=TRUE,
>pl=TRUE,
>family="threshold")
>
> summary(dep)
>
> summary(glm(parity~log.med.depth, data=traits,
> family=binomial(link=probit)))
>
> Cheers,
>
> Jarrod
>
>
>
> On 15/12/2016 20:59, Chris Mull wrote:
>
> Hi All,
> I am trying to look at the correlated evolution of traits using the
> threshold model as implemented in phytools::threshBayes (Revell 2014) and
> MCMCglmmRAM (Hadfield 2015). My understanding from Hadfield 2015 is that
> the reduced animal models should yeild equivalent results, yet having run
> both I am having trouble reconciling the results. I have a tree covering
> ~600 species of sharks. skates, and rays and am interested in testing for
> the correlated evolution between reproductive mode (egg-laying and
> live-bearing) with depth. When I test for this correlation using
> phytools:threshBayes there is a clear result with a high correlation
> coefficient (-0.986) as I would predict. When I run the same analysis using
> MCMCglmmRAM I get a very different result, with

Re: [R-sig-phylo] Threshold models using threshBayes vs MCMCglmmRAM

2016-12-15 Thread Chris Mull

Hi Jarrod,
Thanks very much for your fast reply. Egg-laying and live-bearing are
dispersed throughout the tree ( I have attached a PDF of a traitplot with
egg-laying and live-bearing on it; blue is egg-laying and red is
live-bearing), being universal in chimaeras and skates, and found in
several families of galeomorph sharks. Here are the summaries of the two
models:

#
>summary(dep)
Iterations = 3001:12991
 Thinning interval  = 10
 Sample size  = 1000

 DIC: 62.7561

 G-structure:  ~animal

   post.mean l-95% CI u-95% CI eff.samp
animal 1110

 R-structure:  ~units

  post.mean l-95% CI u-95% CI eff.samp
units 1110

 Location effects: parity ~ log.med.depth

  post.mean l-95% CI u-95% CI eff.samp pMCMC
(Intercept) 0.13854 -0.97336  1.4057652.98  0.87
log.med.depth  -0.06105 -0.37972  0.3212214.31  0.69

#
>summary(glm)
Call:
glm(formula = parity ~ log.med.depth, family = binomial(link = probit),
data = traits)

Deviance Residuals:
Min   1Q   Median   3Q  Max
-2.5976  -1.0564   0.5410   0.8522   1.6867

Coefficients:
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.6195 0.2428  10.789   <2e-16 ***
log.med.depth  -0.9815 0.1030  -9.526   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 784.17  on 609  degrees of freedom
Residual deviance: 683.16  on 608  degrees of freedom
AIC: 687.16

Number of Fisher Scoring iterations: 4


Please let me know if there is any more info I can provide...


Cheers,
Chris

On Thu, Dec 15, 2016 at 11:02 PM, Jarrod Hadfield 
wrote:

> Hi Chris,
>
> I think ngen in threshbayes is not the number of full iterations (i.e. a
> full update of all parameters), but the number of full iterations
> multiplied by the number of nodes (2n-1). With n=600 species this means
> threshbayes has only really done about 8,000 iterations (i.e. about 1000X
> less than MCMCglmm). Simulations suggest threshbayes is about half as
> efficient per full iteration as MCMCglmm which means that it may have only
> collected 0.5*1132/1200 = 0.47 effective samples from the posterior. The
> very extreme value and the surprisingly tight credible intervals (+/-0.007)
> also suggest a problem.
>
> **However**, the low effective sample size for the covariance in the
> MCMglmm model is also worrying given the length of chain, and may point to
> potential problems. Are egg-laying/live-bearing scattered throughout the
> tree, or do they tend to aggregate a lot? Can you send me the output from:
>
> prior.dep<-=list(R=list(V=diag(1)*1e-15, fix=1),
> G=list(G1=list(V=diag(1), fix=1)))
>
> dep<-MCMCglmm(parity~log.med.depth,
>random=~animal,
>rcov=~units,
>pedigree=shark.tree,
>reduced=TRUE,
>data=traits,
>prior=prior2,
>pr=TRUE,
>pl=TRUE,
>family="threshold")
>
> summary(dep)
>
> summary(glm(parity~log.med.depth, data=traits,
> family=binomial(link=probit)))
>
> Cheers,
>
> Jarrod
>
>
>
> On 15/12/2016 20:59, Chris Mull wrote:
>
> Hi All,
> I am trying to look at the correlated evolution of traits using the
> threshold model as implemented in phytools::threshBayes (Revell 2014) and
> MCMCglmmRAM (Hadfield 2015). My understanding from Hadfield 2015 is that
> the reduced animal models should yeild equivalent results, yet having run
> both I am having trouble reconciling the results. I have a tree covering
> ~600 species of sharks. skates, and rays and am interested in testing for
> the correlated evolution between reproductive mode (egg-laying and
> live-bearing) with depth. When I test for this correlation using
> phytools:threshBayes there is a clear result with a high correlation
> coefficient (-0.986) as I would predict. When I run the same analysis using
> MCMCglmmRAM I get a very different result, with a low degree of covariation
> and CI crossing zero (-0.374; -3.638 - 3.163 95%CI). Both models are run
> for 10,000,000 generations with the same bunr-in and sampling period. I
> have looked at the trace plots for each model using coda and parameter
> estimates and likelihodd . I'm not sure how to reconcile the differences in
> these results and any advice would be greatly appreciated. I have appended
> the code and outputs below.
>
>
> ###
> #phytools::threshBayes#
> ###
>
> X<-shark.data[c("parity","log.med.depth")]
> X<-as.matrix(X)
>
> #mcmc paramters (also see control options)
> ngen<-1000
> burnin<-0.2*ngen
> sample<-500
>
> thresh<-threshBayes(shark.tree,
>X,
>types=c("discrete","continuous"),
>

Re: [R-sig-phylo] Threshold models using threshBayes vs MCMCglmmRAM

2016-12-15 Thread Jarrod Hadfield


Hi Chris,

I think MCMCglmm is probably giving you the right answer. There are huge 
chunks of the phylogeny that are either egg-laying and live-bearing. The 
non-phylogenetic model shows a strong relationship between reproductive 
mode and depth, and that might be causal or it might just be because 
certain taxa tend to live at greater depths and *happen to have* the 
same reproductive mode. There's not much information in the phylogenetic 
spread of reproductive modes to distinguish between these hypotheses, 
hence the wide confidence intervals.   Just to be sure can you


a) just perform independent contrasts (not really suitable for binary 
data, but probably won't give you an answer far off the truth and is a 
nice simple sanity check).


b) using MCMCglmm (not MCMCglmmRAM) fit

prior.dep2<-=list(R=list(V=diag(1), fix=1), G=list(G1=list(V=diag(1), 
nu=0.002)))


dep2<-MCMCglmm(parity~log.med.depth,
   random=~animal,
   rcov=~units,
   pedigree=shark.tree,
   data=traits,
   prior=prior.dep2,
   pr=TRUE,
   pl=TRUE,
   family="threshold")

an send me the summary and hist(dep2$Liab)

Cheers,

Jarrod



On 16/12/2016 07:02, Jarrod Hadfield wrote:


Hi Chris,

I think ngen in threshbayes is not the number of full iterations (i.e. 
a full update of all parameters), but the number of full iterations 
multiplied by the number of nodes (2n-1). With n=600 species this 
means threshbayes has only really done about 8,000 iterations (i.e. 
about 1000X less than MCMCglmm). Simulations suggest threshbayes is 
about half as efficient per full iteration as MCMCglmm which means 
that it may have only collected 0.5*1132/1200 = 0.47 effective samples 
from the posterior. The very extreme value and the surprisingly tight 
credible intervals (+/-0.007) also suggest a problem.


*However*, the low effective sample size for the covariance in the 
MCMglmm model is also worrying given the length of chain, and may 
point to potential problems. Are egg-laying/live-bearing scattered 
throughout the tree, or do they tend to aggregate a lot? Can you send 
me the output from:


prior.dep<-=list(R=list(V=diag(1)*1e-15, fix=1), 
G=list(G1=list(V=diag(1), fix=1)))


dep<-MCMCglmm(parity~log.med.depth,
   random=~animal,
   rcov=~units,
   pedigree=shark.tree,
   reduced=TRUE,
   data=traits,
   prior=prior2,
   pr=TRUE,
   pl=TRUE,
   family="threshold")

summary(dep)

summary(glm(parity~log.med.depth, data=traits, 
family=binomial(link=probit)))


Cheers,

Jarrod



On 15/12/2016 20:59, Chris Mull wrote:

Hi All,
I am trying to look at the correlated evolution of traits using the
threshold model as implemented in phytools::threshBayes (Revell 2014) and
MCMCglmmRAM (Hadfield 2015). My understanding from Hadfield 2015 is that
the reduced animal models should yeild equivalent results, yet having run
both I am having trouble reconciling the results. I have a tree covering
~600 species of sharks. skates, and rays and am interested in testing for
the correlated evolution between reproductive mode (egg-laying and
live-bearing) with depth. When I test for this correlation using
phytools:threshBayes there is a clear result with a high correlation
coefficient (-0.986) as I would predict. When I run the same analysis using
MCMCglmmRAM I get a very different result, with a low degree of covariation
and CI crossing zero (-0.374; -3.638 - 3.163 95%CI). Both models are run
for 10,000,000 generations with the same bunr-in and sampling period. I
have looked at the trace plots for each model using coda and parameter
estimates and likelihodd . I'm not sure how to reconcile the differences in
these results and any advice would be greatly appreciated. I have appended
the code and outputs below.


###
#phytools::threshBayes#
###

X<-shark.data[c("parity","log.med.depth")]
X<-as.matrix(X)

#mcmc paramters (also see control options)
ngen<-1000
burnin<-0.2*ngen
sample<-500

thresh<-threshBayes(shark.tree,
X,
types=c("discrete","continuous"),
ngen=ngen,
control = list(sample=sample))

The return correlation is -0.986 (-0.99 - -0.976  95%HPD)


#
#MCMCglmm bivariate-gaussian#
#


prior2=list(R=list(V=diag(2)*1e-15, fix=1), G=list(G1=list(V=diag(2),

nu=0.002, fix=2)))

ellb.log.dep<-MCMCglmm(cbind(log.med.depth,parity)~trait-1,
   random=~us(trait):animal,
   rcov=~us(trait):units,
   pedigree=shark.tree,
   reduced=TRUE,
   data=traits,

Re: [R-sig-phylo] Threshold models using threshBayes vs MCMCglmmRAM

2016-12-15 Thread Jarrod Hadfield


Hi Chris,

I think ngen in threshbayes is not the number of full iterations (i.e. a 
full update of all parameters), but the number of full iterations 
multiplied by the number of nodes (2n-1). With n=600 species this means 
threshbayes has only really done about 8,000 iterations (i.e. about 
1000X less than MCMCglmm). Simulations suggest threshbayes is about half 
as efficient per full iteration as MCMCglmm which means that it may have 
only collected 0.5*1132/1200 = 0.47 effective samples from the 
posterior. The very extreme value and the surprisingly tight credible 
intervals (+/-0.007) also suggest a problem.


*However*, the low effective sample size for the covariance in the 
MCMglmm model is also worrying given the length of chain, and may point 
to potential problems. Are egg-laying/live-bearing scattered throughout 
the tree, or do they tend to aggregate a lot? Can you send me the output 
from:


prior.dep<-=list(R=list(V=diag(1)*1e-15, fix=1), 
G=list(G1=list(V=diag(1), fix=1)))


dep<-MCMCglmm(parity~log.med.depth,
   random=~animal,
   rcov=~units,
   pedigree=shark.tree,
   reduced=TRUE,
   data=traits,
   prior=prior2,
   pr=TRUE,
   pl=TRUE,
   family="threshold")

summary(dep)

summary(glm(parity~log.med.depth, data=traits, 
family=binomial(link=probit)))


Cheers,

Jarrod



On 15/12/2016 20:59, Chris Mull wrote:

Hi All,
I am trying to look at the correlated evolution of traits using the
threshold model as implemented in phytools::threshBayes (Revell 2014) and
MCMCglmmRAM (Hadfield 2015). My understanding from Hadfield 2015 is that
the reduced animal models should yeild equivalent results, yet having run
both I am having trouble reconciling the results. I have a tree covering
~600 species of sharks. skates, and rays and am interested in testing for
the correlated evolution between reproductive mode (egg-laying and
live-bearing) with depth. When I test for this correlation using
phytools:threshBayes there is a clear result with a high correlation
coefficient (-0.986) as I would predict. When I run the same analysis using
MCMCglmmRAM I get a very different result, with a low degree of covariation
and CI crossing zero (-0.374; -3.638 - 3.163 95%CI). Both models are run
for 10,000,000 generations with the same bunr-in and sampling period. I
have looked at the trace plots for each model using coda and parameter
estimates and likelihodd . I'm not sure how to reconcile the differences in
these results and any advice would be greatly appreciated. I have appended
the code and outputs below.


###
#phytools::threshBayes#
###

X<-shark.data[c("parity","log.med.depth")]
X<-as.matrix(X)

#mcmc paramters (also see control options)
ngen<-1000
burnin<-0.2*ngen
sample<-500

thresh<-threshBayes(shark.tree,
X,
types=c("discrete","continuous"),
ngen=ngen,
control = list(sample=sample))

The return correlation is -0.986 (-0.99 - -0.976  95%HPD)


#
#MCMCglmm bivariate-gaussian#
#


prior2=list(R=list(V=diag(2)*1e-15, fix=1), G=list(G1=list(V=diag(2),

nu=0.002, fix=2)))

ellb.log.dep<-MCMCglmm(cbind(log.med.depth,parity)~trait-1,
   random=~us(trait):animal,
   rcov=~us(trait):units,
   pedigree=shark.tree,
   reduced=TRUE,
   data=traits,
   prior=prior2,
   pr=TRUE,
   pl=TRUE,
   family=c("gaussian", "threshold"),
   thin=500,
   burnin = 100,
   nitt = 1000)

summary(ellb.log.dep)

Iterations = 101:501
Thinning interval  = 500
Sample size  = 18000
DIC: 2930.751
G-structure:  ~us(trait):animal
 post.mean l-95% CI u-95% CI eff.samp
traitscale.depth:traitscale.depth.animal16.965   15.092   18.864
  18000
traitparity:traitscale.depth.animal -0.374   -3.6383.163
1132
traitscale.depth:traitparity.animal -0.374   -3.6383.163
1132
traitparity:traitparity.animal   1.0001.0001.000
  0
R-structure:  ~us(trait):units
post.mean l-95% CI u-95% CI eff.samp
traitscale.depth:traitscale.depth.units16.965   15.092   18.86418000
traitparity:traitscale.depth.units -0.374   -3.6383.163 1132
traitscale.depth:traitparity.units -0.374   -3.6383.163 1132
traitparity:traitparity.units   1.0001.0001.0000
Location effects: cbind(scale.depth, parity) ~ trait - 1
 post.mean l-95% CI u-95% CI eff.samp pMCMC

[R-sig-phylo] Threshold models using threshBayes vs MCMCglmmRAM

2016-12-15 Thread Chris Mull

Hi All,
I am trying to look at the correlated evolution of traits using the
threshold model as implemented in phytools::threshBayes (Revell 2014) and
MCMCglmmRAM (Hadfield 2015). My understanding from Hadfield 2015 is that
the reduced animal models should yeild equivalent results, yet having run
both I am having trouble reconciling the results. I have a tree covering
~600 species of sharks. skates, and rays and am interested in testing for
the correlated evolution between reproductive mode (egg-laying and
live-bearing) with depth. When I test for this correlation using
phytools:threshBayes there is a clear result with a high correlation
coefficient (-0.986) as I would predict. When I run the same analysis using
MCMCglmmRAM I get a very different result, with a low degree of covariation
and CI crossing zero (-0.374; -3.638 - 3.163 95%CI). Both models are run
for 10,000,000 generations with the same bunr-in and sampling period. I
have looked at the trace plots for each model using coda and parameter
estimates and likelihodd . I'm not sure how to reconcile the differences in
these results and any advice would be greatly appreciated. I have appended
the code and outputs below.


###
#phytools::threshBayes#
###
>X<-shark.data[c("parity","log.med.depth")]
>X<-as.matrix(X)
>
>#mcmc paramters (also see control options)
>ngen<-1000
>burnin<-0.2*ngen
>sample<-500
>
>thresh<-threshBayes(shark.tree,
>X,
>types=c("discrete","continuous"),
>ngen=ngen,
>control = list(sample=sample))

The return correlation is -0.986 (-0.99 - -0.976  95%HPD)


#
#MCMCglmm bivariate-gaussian#
#

>prior2=list(R=list(V=diag(2)*1e-15, fix=1), G=list(G1=list(V=diag(2),
nu=0.002, fix=2)))
>
>ellb.log.dep<-MCMCglmm(cbind(log.med.depth,parity)~trait-1,
>   random=~us(trait):animal,
>   rcov=~us(trait):units,
>   pedigree=shark.tree,
>   reduced=TRUE,
>   data=traits,
>   prior=prior2,
>   pr=TRUE,
>   pl=TRUE,
>   family=c("gaussian", "threshold"),
>   thin=500,
>   burnin = 100,
>   nitt = 1000)
>
>summary(ellb.log.dep)

Iterations = 101:501
Thinning interval  = 500
Sample size  = 18000
DIC: 2930.751
G-structure:  ~us(trait):animal
post.mean l-95% CI u-95% CI eff.samp
traitscale.depth:traitscale.depth.animal16.965   15.092   18.864
 18000
traitparity:traitscale.depth.animal -0.374   -3.6383.163
1132
traitscale.depth:traitparity.animal -0.374   -3.6383.163
1132
traitparity:traitparity.animal   1.0001.0001.000
 0
R-structure:  ~us(trait):units
   post.mean l-95% CI u-95% CI eff.samp
traitscale.depth:traitscale.depth.units16.965   15.092   18.86418000
traitparity:traitscale.depth.units -0.374   -3.6383.163 1132
traitscale.depth:traitparity.units -0.374   -3.6383.163 1132
traitparity:traitparity.units   1.0001.0001.0000
Location effects: cbind(scale.depth, parity) ~ trait - 1
post.mean l-95% CI u-95% CI eff.samp pMCMC
traitscale.depth   0.12297 -3.63655  4.0200518000 0.949
traitparity   -0.02212 -1.00727  0.9338717058 0.971

Thanks for any and all input.

Cheers,
Chris

-- 
Christopher Mull
PhD Candidate, Shark Biology and Evolutionary Neuroecology
Dulvy Lab
Simon Fraser University
Burnaby,B.C.
V5A 1S6
Canada
(778) 782-3989
twitter: @mrsharkbrain
e-mail:cm...@sfu.ca
www.christophermull.weebly.com
www.earth2ocean.org

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Re: [R-sig-phylo] Threshold models using threshBayes vs MCMCglmmRAM

Re: [R-sig-phylo] Threshold models using threshBayes vs MCMCglmmRAM

Re: [R-sig-phylo] Threshold models using threshBayes vs MCMCglmmRAM

Re: [R-sig-phylo] Threshold models using threshBayes vs MCMCglmmRAM

Re: [R-sig-phylo] Threshold models using threshBayes vs MCMCglmmRAM

[R-sig-phylo] Threshold models using threshBayes vs MCMCglmmRAM

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